cluster.py 文件源码

def cluster(centers):
    n_class = int(len(centers) * 0.18)
    est = KMeans(n_clusters=n_class, max_iter=1000)
    est.fit(centers)
    new_list = []
    for x, y in est.cluster_centers_:
        min_num = 10000
        min_x = -1
        min_y = -1
        for x_, y_ in centers:
            dist = distance(x, y, x_, y_)
            if (dist < min_num) or (min_x == -1):
                min_num = dist
                min_x = x_
                min_y = y_
        new_list.append([min_x, min_y])
    return new_list